REDCapCAST/articles/Database-creation.md

# Database-creation

``` r
library(REDCapCAST)
```

## Two different ways to create a data base

`REDCapCAST` provides two approaches to creating a data dictionary aimed
at helping out in two different cases:

1.  Easily create a REDCap data base from an existing data set.

2.  Create a table in Word describing a variables in a data base and use
    this to create a data base.

In the following I will try to come with a few suggestions on how to use
these approaches.

### Easy data set to data base workflow

The first iteration of a dataset to data dictionary function is the
[`ds2dd()`](https://agdamsbo.github.io/REDCapCAST/reference/ds2dd.md),
which creates a very basic data dictionary with all variables stored as
text. This is sufficient for just storing old datasets/spreadsheets
securely in REDCap.

``` r
d1 <- mtcars |>
  dplyr::mutate(record_id = seq_len(dplyr::n())) |>
  ds2dd() 

d1 |>
  gt::gt()
```

The more advanced
[`ds2dd_detailed()`](https://agdamsbo.github.io/REDCapCAST/reference/ds2dd_detailed.md)
is a natural development. It will try to apply the most common data
classes for data validation and will assume that the first column is the
id number. It outputs a list with the dataset with modified variable
names to comply with REDCap naming conventions and a data dictionary.

The dataset should be correctly formatted for the data dictionary to
preserve as much information as possible.

``` r
d2 <- REDCapCAST::redcapcast_data |> 
  dplyr::mutate(record_id = seq_len(dplyr::n()),
                region=factor(region)) |>
  dplyr::select(record_id, dplyr::everything()) |>
  (\(.x){
    .x[!grepl("_complete$",names(.x))]
  })() |> 
  (\(.x){
    .x[!grepl("^redcap",names(.x))]
  })() |>  
  ds2dd_detailed() |> 
  purrr::pluck("meta") 

d2 |> 
  gt::gt()
```

Additional specifications to the DataDictionary can be made manually, or
it can be uploaded and modified manually in the graphical user interface
on the REDCap server.

### Data base from table

…instructions and examples are coming…

### Meta data and data upload

Now the DataDictionary can be exported as a spreadsheet and uploaded or
it can be uploaded using the `REDCapR` package (only projects with
“Development” status).

Use one of the two approaches below:

#### Manual upload

``` r
write.csv(dd_ls$meta, "datadictionary.csv")
```

#### Upload with `REDCapR`

``` r
REDCapR::redcap_metadata_write(
  dd_ls$meta,
  redcap_uri = keyring::key_get("DB_URI"),
  token = keyring::key_get("DB_TOKEN")
)
```

In the [“REDCap R
Handbook”](https://agdamsbo.github.io/redcap-r-handbook/) more is
written on interfacing with REDCap in R using the
[`library(keyring)`](https://keyring.r-lib.org/)to store credentials in
[chapter
1.1](https://agdamsbo.github.io/redcap-r-handbook/doc/access.html#sec-getting-access).

### Step 4 - Data upload

The same two options are available for data upload as meta data upload:
manual or through `REDCapR`.

Only the latter is shown here.

``` r
REDCapR::redcap_write(
  dd_ls$data,
  redcap_uri = keyring::key_get("DB_URI"),
  token = keyring::key_get("DB_TOKEN")
)
```